Q1: What is the trend in cases, mortality across geopgraphical regions?
Plot # of cases vs time
* For each geographical set:
* comparative longitudinal case trend (absolute & log scale)
* comparative longitudinal mortality trend
* death vs total correlation
| comparative_longitudinal_case_trend |
long |
time |
log_cases |
geography |
none (case type?) |
case_type |
[15, 50, 4] geography x (2 scale?) case type |
| comparative longitudinal case trend |
long |
time |
cases |
geography |
case_type |
? |
[15, 50, 4] geography x (2+ scale) case type |
| comparative longitudinal mortality trend |
wide |
time |
mortality rate |
geography |
none |
none |
[15, 50, 4] geography |
| death vs total correlation |
wide |
cases |
deaths |
geography |
none |
none |
[15, 50, 4] geography |
# total cases vs time
# death cases vs time
# mortality rate vs time
# death vs mortality
# death vs mortality
# total & death case vs time (same plot)
#<question> <x> <y> <colored> <facet> <dataset>
## trend in case/deaths over time, comapred across regions <time> <log cases> <geography*> <none> <.wide>
## trend in case/deaths over time, comapred across regions <time> <cases> <geography*> <case_type> <.long>
## trend in mortality rate over time, comapred across regions <time> <mortality rate> <geography*> <none>
## how are death/mortality related/correlated? <time> <log cases> <geography*> <none>
## how are death and case load correlated? <cases> <deaths>
# lm for each?? - > apply lm from each region starting from 100th case. m, b associated with each.
# input: geographical regsion, logcase vs day (100th case)
# output: m, b for each geographical region ID
#total/death on same plot- diffeer by 2 logs, so when plotting log, use pch. when plotting absolute, need to use free scales
#when plotting death and case on same, melt.
#CoronaCases - > filter sets (3)
#world - choose countries with sufficent data
N<-ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,n=length(Country.Region))
ggplot(filter(N,n<100),aes(x=n))+
geom_histogram()+
default_theme+
ggtitle("Distribution of number of days with at least 100 confirmed cases for each region")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

kable(arrange(N,-n),caption="Sorted number of days with at least 100 confirmed cases")
Sorted number of days with at least 100 confirmed cases
| US_state |
63826 |
| China |
148 |
| Diamond Princess |
129 |
| Korea, South |
119 |
| Japan |
118 |
| Italy |
116 |
| Iran |
113 |
| Singapore |
110 |
| France |
109 |
| Germany |
109 |
| Spain |
108 |
| US |
106 |
| Switzerland |
105 |
| United Kingdom |
105 |
| Belgium |
104 |
| Netherlands |
104 |
| Norway |
104 |
| Sweden |
104 |
| Austria |
102 |
| Malaysia |
101 |
| Australia |
100 |
| Bahrain |
100 |
| Denmark |
100 |
| Canada |
99 |
| Qatar |
99 |
| Iceland |
98 |
| Brazil |
97 |
| Czechia |
97 |
| Finland |
97 |
| Greece |
97 |
| Iraq |
97 |
| Israel |
97 |
| Portugal |
97 |
| Slovenia |
97 |
| Egypt |
96 |
| Estonia |
96 |
| India |
96 |
| Ireland |
96 |
| Kuwait |
96 |
| Philippines |
96 |
| Poland |
96 |
| Romania |
96 |
| Saudi Arabia |
96 |
| Indonesia |
95 |
| Lebanon |
95 |
| Pakistan |
95 |
| San Marino |
95 |
| Thailand |
95 |
| Chile |
94 |
| Luxembourg |
93 |
| Peru |
93 |
| Russia |
93 |
| Ecuador |
92 |
| Mexico |
92 |
| Slovakia |
92 |
| South Africa |
92 |
| United Arab Emirates |
92 |
| Armenia |
91 |
| Colombia |
91 |
| Croatia |
91 |
| Panama |
91 |
| Serbia |
91 |
| Taiwan* |
91 |
| Turkey |
91 |
| Argentina |
90 |
| Bulgaria |
90 |
| Latvia |
90 |
| Uruguay |
90 |
| Algeria |
89 |
| Costa Rica |
89 |
| Dominican Republic |
89 |
| Hungary |
89 |
| Andorra |
88 |
| Bosnia and Herzegovina |
88 |
| Jordan |
88 |
| Lithuania |
88 |
| Morocco |
88 |
| New Zealand |
88 |
| North Macedonia |
88 |
| Vietnam |
88 |
| Albania |
87 |
| Cyprus |
87 |
| Malta |
87 |
| Moldova |
87 |
| Brunei |
86 |
| Burkina Faso |
86 |
| Sri Lanka |
86 |
| Tunisia |
86 |
| Ukraine |
85 |
| Azerbaijan |
84 |
| Ghana |
84 |
| Kazakhstan |
84 |
| Oman |
84 |
| Senegal |
84 |
| Venezuela |
84 |
| Afghanistan |
83 |
| Cote d’Ivoire |
83 |
| Cuba |
82 |
| Mauritius |
82 |
| Uzbekistan |
82 |
| Cambodia |
81 |
| Cameroon |
81 |
| Honduras |
81 |
| Nigeria |
81 |
| West Bank and Gaza |
81 |
| Belarus |
80 |
| Georgia |
80 |
| Bolivia |
79 |
| Kosovo |
79 |
| Kyrgyzstan |
79 |
| Montenegro |
79 |
| Congo (Kinshasa) |
78 |
| Kenya |
77 |
| Niger |
76 |
| Guinea |
75 |
| Rwanda |
75 |
| Trinidad and Tobago |
75 |
| Paraguay |
74 |
| Bangladesh |
73 |
| Djibouti |
71 |
| El Salvador |
70 |
| Guatemala |
69 |
| Madagascar |
68 |
| Mali |
67 |
| Congo (Brazzaville) |
64 |
| Jamaica |
64 |
| Gabon |
62 |
| Somalia |
62 |
| Tanzania |
62 |
| Ethiopia |
61 |
| Burma |
60 |
| Sudan |
59 |
| Liberia |
58 |
| Maldives |
56 |
| Equatorial Guinea |
55 |
| Cabo Verde |
53 |
| Sierra Leone |
51 |
| Guinea-Bissau |
50 |
| Togo |
50 |
| Zambia |
49 |
| Eswatini |
48 |
| Chad |
47 |
| Tajikistan |
46 |
| Haiti |
44 |
| Sao Tome and Principe |
44 |
| Benin |
42 |
| Nepal |
42 |
| Uganda |
42 |
| Central African Republic |
41 |
| South Sudan |
41 |
| Guyana |
39 |
| Mozambique |
38 |
| Yemen |
34 |
| Mongolia |
33 |
| Mauritania |
30 |
| Nicaragua |
30 |
| Malawi |
24 |
| Syria |
24 |
| Zimbabwe |
22 |
| Bahamas |
21 |
| Libya |
21 |
| Comoros |
19 |
| Suriname |
11 |
| Angola |
8 |
| Eritrea |
3 |
| Burundi |
2 |
# Pick top 15 countries with data
max_colors<-12
# find way to fix this- China has diff provences. Plot doesnt look right...
sufficient_data<-arrange(filter(N,!Country.Region %in% c("US_state", "Diamond Princess")),-n)[1:max_colors,]
kable(sufficient_data,caption = paste0("Top ",max_colors," countries with sufficient data"))
Top 12 countries with sufficient data
| China |
148 |
| Korea, South |
119 |
| Japan |
118 |
| Italy |
116 |
| Iran |
113 |
| Singapore |
110 |
| France |
109 |
| Germany |
109 |
| Spain |
108 |
| US |
106 |
| Switzerland |
105 |
| United Kingdom |
105 |
Corona_Cases.world<-filter(Corona_Cases,Country.Region %in% c(sufficient_data$Country.Region))
#us
# - by state
Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
# summarize
#!City %in% c("Unassigned")
# - specific cities
#mortality_rate!=Inf & mortality_rate<=1
head(Corona_Cases)
## Country.Region Province.State City Date Date.numeric
## 1 Afghanistan <NA> <NA> 2020-04-17 18369
## 2 Afghanistan <NA> <NA> 2020-04-16 18368
## 3 Afghanistan <NA> <NA> 2020-04-15 18367
## 4 Afghanistan <NA> <NA> 2020-06-02 18415
## 5 Afghanistan <NA> <NA> 2020-04-26 18378
## 6 Afghanistan <NA> <NA> 2020-04-19 18371
## Total_confirmed_deaths Total_confirmed_cases mortality_rate
## 1 30 906 0.03311258
## 2 30 840 0.03571429
## 3 25 784 0.03188776
## 4 270 16509 0.01635472
## 5 50 1531 0.03265839
## 6 33 996 0.03313253
## Total_confirmed_cases.log Total_confirmed_deaths.log case100_date
## 1 2.957128 1.477121 18348
## 2 2.924279 1.477121 18348
## 3 2.894316 1.397940 18348
## 4 4.217721 2.431364 18348
## 5 3.184975 1.698970 18348
## 6 2.998259 1.518514 18348
## Days_since_100 Lat Long Population Total_confirmed_cases.per100
## 1 21 NA NA NA NA
## 2 20 NA NA NA NA
## 3 19 NA NA NA NA
## 4 67 NA NA NA NA
## 5 30 NA NA NA NA
## 6 23 NA NA NA NA
## Total_confirmed_deaths.per100
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
Corona_Cases[!is.na(Corona_Cases$Province.State) & Corona_Cases$Province.State=="Pennsylvania" & Corona_Cases$City=="Delaware","City"]<-"Delaware_PA"
Corona_Cases.UScity<-filter(Corona_Cases,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") & City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May","Delaware_PA"))
measure_vars_long<-c("Total_confirmed_cases.log","Total_confirmed_cases","Total_confirmed_deaths","Total_confirmed_deaths.log")
melt_arg_list<-list(variable.name = "case_type",value.name = "cases",measure.vars = c("Total_confirmed_cases","Total_confirmed_deaths"))
melt_arg_list$data=NULL
melt_arg_list$data=select(Corona_Cases.world,-ends_with(match = "log"))
Corona_Cases.world.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.UScity,-ends_with(match = "log"))
Corona_Cases.UScity.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.US_state,-ends_with(match = "log"))
Corona_Cases.US_state.long<-do.call(melt,melt_arg_list)
Corona_Cases.world.long$cases.log<-log(Corona_Cases.world.long$cases,10)
Corona_Cases.US_state.long$cases.log<-log(Corona_Cases.US_state.long$cases,10)
Corona_Cases.UScity.long$cases.log<-log(Corona_Cases.UScity.long$cases,10)
# what is the current death and total case load for US? For world? For states?
#-absolute
#-log
# what is mortality rate (US, world)
#-absolute
#how is death and case correlated? (US, world)
#-absolute
#Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
#Corona_Cases.US.case100<-filter(Corona_Cases.US, Days_since_100>=0)
# linear model parameters
#(model_fit<-lm(formula = Total_confirmed_cases.log~Days_since_100,data= Corona_Cases.US.case100 ))
#(slope<-model_fit$coefficients[2])
#(intercept<-model_fit$coefficients[1])
# Correlation coefficient
#cor(x = Corona_Cases.US.case100$Days_since_100,y = Corona_Cases.US.case100$Total_confirmed_cases.log)
##------------------------------------------
## Plot World Data
##------------------------------------------
# Timestamp for world
timestamp_plot.world<-paste("Most recent date for which data available:",max(Corona_Cases.world$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")
# Base template for plots
baseplot.world<-ggplot(data=NULL,aes(x=Days_since_100,col=Country.Region))+
default_theme+
scale_color_brewer(type = "qualitative",palette = "Paired")+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
theme(legend.position = "bottom",plot.title = element_text(size=12))
##/////////////////////////
### Plot Longitudinal cases
(Corona_Cases.world.long.plot<-baseplot.world+
geom_point(data=Corona_Cases.world.long,aes(y=cases))+
geom_line(data=Corona_Cases.world.long,aes(y=cases))+
facet_wrap(~case_type,scales = "free_y",ncol=1)+
ggtitle(timestamp_plot.world)
)

(Corona_Cases.world.loglong.plot<-baseplot.world+
geom_point(data=Corona_Cases.world.long,aes(y=cases.log))+
geom_line(data=Corona_Cases.world.long,aes(y=cases.log))+
facet_wrap(~case_type,scales = "free_y",ncol=1)+
ggtitle(timestamp_plot.world))

##/////////////////////////
### Plot Longitudinal mortality rate
(Corona_Cases.world.mortality.plot<-baseplot.world+
geom_point(data=Corona_Cases.world,aes(y=mortality_rate))+
geom_line(data=Corona_Cases.world,aes(y=mortality_rate))+
ylim(c(0,0.3))+
ggtitle(timestamp_plot.world))
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).

##/////////////////////////
### Plot death vs total case correlation
(Corona_Cases.world.casecor.plot<-ggplot(Corona_Cases.world,aes(x=Total_confirmed_cases,y=Total_confirmed_deaths,col=Country.Region))+
geom_point()+
geom_line()+
default_theme+
scale_color_brewer(type = "qualitative",palette = "Paired")+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
theme(legend.position = "bottom",plot.title = element_text(size=12))+
ggtitle(timestamp_plot.world))

### Write polots
write_plot(Corona_Cases.world.long.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.long.plot.png"
write_plot(Corona_Cases.world.loglong.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.loglong.plot.png"
write_plot(Corona_Cases.world.mortality.plot,wd = results_dir)
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.mortality.plot.png"
write_plot(Corona_Cases.world.casecor.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.casecor.plot.png"
##------------------------------------------
## Plot US State Data
##-----------------------------------------
baseplot.US<-ggplot(data=NULL,aes(x=Days_since_100_state,col=case_type))+
default_theme+
facet_wrap(~Province.State)+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))
Corona_Cases.US_state.long.plot<-baseplot.US+geom_point(data=Corona_Cases.US_state.long,aes(y=cases.log))
##------------------------------------------
## Plot US City Data
##-----------------------------------------
Corona_Cases.US.plotdata<-filter(Corona_Cases.US_state,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") &
City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May","Delaware_PA") &
Total_confirmed_cases>0)
timestamp_plot<-paste("Most recent date for which data available:",max(Corona_Cases.US.plotdata$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")
city_colors<-c("Bucks"='#beaed4',"Baltimore City"='#386cb0', "New York"='#7fc97f',"Burlington"='#fdc086',"Cape May"="#e78ac3","Delaware_PA"="#b15928")
##/////////////////////////
### Plot death vs total case correlation
(Corona_Cases.city.loglong.plot<-ggplot(melt(Corona_Cases.US.plotdata,measure.vars = c("Total_confirmed_cases.log","Total_confirmed_deaths.log"),variable.name = "case_type",value.name = "cases"),aes(x=Date,y=cases,col=City,pch=case_type))+
geom_point(size=4)+
geom_line()+
default_theme+
#facet_wrap(~case_type)+
ggtitle(paste("Log10 total and death cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.long.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases,col=City))+
geom_point(size=4)+
geom_line()+
default_theme+
facet_grid(~Province.State,scales = "free_y")+
ggtitle(paste("MD, PA, NJ total cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))
+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.mortality.plot<-ggplot(Corona_Cases.US.plotdata,aes(x=Date,y=mortality_rate,col=City))+
geom_point(size=3)+
geom_line(size=2)+
default_theme+
ggtitle(paste("Mortality rate (deaths/total) over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.casecor.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(y=Total_confirmed_deaths,x=Total_confirmed_cases,col=City))+
geom_point(size=3)+
geom_line(size=2)+
default_theme+
ggtitle(paste("Correlation of death vs total cases,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12))+
scale_color_manual(values = city_colors))

(Corona_Cases.city.long.normalized.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases.per100,col=City))+
geom_point(size=4)+
geom_line()+
default_theme+
facet_grid(~Province.State)+
ggtitle(paste("MD, PA, NJ total cases over time per 100 people,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors) +
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

write_plot(Corona_Cases.city.long.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.plot.png"
write_plot(Corona_Cases.city.loglong.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.loglong.plot.png"
write_plot(Corona_Cases.city.mortality.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.mortality.plot.png"
write_plot(Corona_Cases.city.casecor.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.casecor.plot.png"
write_plot(Corona_Cases.city.long.normalized.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.normalized.plot.png"
Q1b what is the model
Fit the cases to a linear model 1. Find time at which the case vs date becomes linear in each plot
2. Fit linear model for each city
# What is the predict # of cases for the next few days?
# How is the model performing historically?
Corona_Cases.US_state.summary<-ddply(Corona_Cases.US_state,
c("Province.State","Date"),
summarise,
Total_confirmed_cases_perstate=sum(Total_confirmed_cases)) %>%
filter(Total_confirmed_cases_perstate>100)
# Compute the states with the most cases (for coloring and for linear model)
top_states_totals<-head(ddply(Corona_Cases.US_state.summary,c("Province.State"),summarise, Total_confirmed_cases_perstate.max=max(Total_confirmed_cases_perstate)) %>% arrange(-Total_confirmed_cases_perstate.max),n=max_colors)
kable(top_states_totals,caption = "Top 12 States, total count ")
top_states<-top_states_totals$Province.State
# Manually fix states so that Maryland is switched out for New York
top_states_modified<-c(top_states[top_states !="New York"],"Maryland")
# Plot with all states:
(Corona_Cases.US_state.summary.plot<-ggplot(Corona_Cases.US_state.summary,aes(x=Date,y=Total_confirmed_cases_perstate))+
geom_point()+
geom_point(data=filter(Corona_Cases.US_state.summary,Province.State %in% top_states),aes(col=Province.State))+
scale_color_brewer(type = "qualitative",palette = "Paired")+
default_theme+
theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
ggtitle("Total confirmed cases per state, top 12 colored")+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))
##------------------------------------------
## Fit linear model to time vs total cases
##-----------------------------------------
# First, find the date at which each state's cases vs time becomes lienar (2nd derivative is about 0)
li<-ddply(Corona_Cases.US_state.summary,c("Province.State"),find_linear_index)
# Compute linear model for each state starting at the point at which data becomes linear
for(i in 1:nrow(li)){
Province.State.i<-li[i,"Province.State"]
date.i<-li[i,"V1"]
data.i<-filter(Corona_Cases.US_state.summary,Province.State==Province.State.i & as.numeric(Date) >= date.i)
model_results<-lm(data.i,formula = Total_confirmed_cases_perstate~Date)
slope<-model_results$coefficients[2]
intercept<-model_results$coefficients[1]
li[li$Province.State==Province.State.i,"m"]<-slope
li[li$Province.State==Province.State.i,"b"]<-intercept
}
# Compute top state case load with fitted model
(Corona_Cases.US_state.lm.plot<-ggplot(filter(Corona_Cases.US_state.summary,Province.State %in% top_states_modified ))+
geom_abline(data=filter(li,Province.State %in% top_states_modified),
aes(slope = m,intercept = b,col=Province.State),lty=2)+
geom_point(aes(x=Date,y=Total_confirmed_cases_perstate,col=Province.State))+
scale_color_brewer(type = "qualitative",palette = "Paired")+
default_theme+
theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
ggtitle("Total confirmed cases per state, top 12 colored")+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))
##------------------------------------------
## Predict the number of total cases over the next week
##-----------------------------------------
predicted_days<-c(0,1,2,3,7)+as.numeric(as.Date("2020-04-20"))
predicted_days_df<-data.frame(matrix(ncol=3))
names(predicted_days_df)<-c("Province.State","days","Total_confirmed_cases_perstate")
# USe model parameters to estiamte case loads
for(state.i in top_states_modified){
predicted_days_df<-rbind(predicted_days_df,
data.frame(Province.State=state.i,
prediction_model(m = li[li$Province.State==state.i,"m"],
b =li[li$Province.State==state.i,"b"] ,
days =predicted_days )))
}
predicted_days_df$Date<-as.Date(predicted_days_df$days,origin="1970-01-01")
kable(predicted_days_df,caption = "Predicted total cases over the next week for selected states")
##------------------------------------------
## Write plots
##-----------------------------------------
write_plot(Corona_Cases.US_state.summary.plot,wd = results_dir)
write_plot(Corona_Cases.US_state.lm.plot,wd = results_dir)
##------------------------------------------
## Write tables
##-----------------------------------------
write.csv(predicted_days_df,file = paste0(results_dir,"predicted_total_cases_days.csv"),quote = F,row.names = F)
Q2: What is the predicted number of cases?
What is the prediction of COVID-19 based on model thus far? Additional questions:
WHy did it take to day 40 to start a log linear trend? How long will it be till x number of cases? When will the plateu happen? Are any effects noticed with social distancing? Delays
##------------------------------------------
## Prediction and Prediction Accuracy
##------------------------------------------
today_num<-max(Corona_Cases.US$Days_since_100)
predicted_days<-today_num+c(1,2,3,7)
#mods = dlply(mydf, .(x3), lm, formula = y ~ x1 + x2)
#today:
Corona_Cases.US[Corona_Cases.US$Days_since_100==(today_num-1),]
Corona_Cases.US[Corona_Cases.US$Days_since_100==today_num,]
Corona_Cases.US$type<-"Historical"
#prediction_values<-prediction_model(m=slope,b=intercept,days = predicted_days)$Total_confirmed_cases
histoical_model<-data.frame(date=today_num,m=slope,b=intercept)
tmp<-data.frame(state=rep(c("A","B"),each=3),x=c(1,2,3,4,5,6))
tmp$y<-c(tmp[1:3,"x"]+5,tmp[4:6,"x"]*5+1)
ddply(tmp,c("state"))
lm(data =tmp,formula = y~x )
train_lm<-function(input_data,subset_coulmn,formula_input){
case_models <- dlply(input_data, subset_coulmn, lm, formula = formula_input)
case_models.parameters <- ldply(case_models, coef)
case_models.parameters<-rename(case_models.parameters,c("b"="(Intercept)","m"=subset_coulmn))
return(case_models.parameters)
}
train_lm(tmp,"state")
dlply(input_data, subset_coulmn, lm,m=)
# model for previous y days
#historical_model_predictions<-data.frame(day_x=NULL,Days_since_100=NULL,Total_confirmed_cases=NULL,Total_confirmed_cases.log=NULL)
# for(i in c(1,2,3,4,5,6,7,8,9,10)){
# #i<-1
# day_x<-today_num-i # 1, 2, 3, 4
# day_x_nextweek<-day_x+c(1,2,3)
# model_fit_x<-lm(data = filter(Corona_Cases.US.case100,Days_since_100 < day_x),formula = Total_confirmed_cases.log~Days_since_100)
# prediction_day_x_nextweek<-prediction_model(m = model_fit_x$coefficients[2],b = model_fit_x$coefficients[1],days = day_x_nextweek)
# prediction_day_x_nextweek$type<-"Predicted"
# acutal_day_x_nextweek<-filter(Corona_Cases.US,Days_since_100 %in% day_x_nextweek) %>% select(c(Days_since_100,Total_confirmed_cases,Total_confirmed_cases.log))
# acutal_day_x_nextweek$type<-"Historical"
# historical_model_predictions.i<-data.frame(day_x=day_x,rbind(acutal_day_x_nextweek,prediction_day_x_nextweek))
# historical_model_predictions<-rbind(historical_model_predictions.i,historical_model_predictions)
# }
#historical_model_predictions.withHx<-rbind.fill(historical_model_predictions,data.frame(Corona_Cases.US,type="Historical"))
#historical_model_predictions.withHx$Total_confirmed_cases.log2<-log(historical_model_predictions.withHx$Total_confirmed_cases,2)
(historical_model_predictions.plot<-ggplot(historical_model_predictions.withHx,aes(x=Days_since_100,y=Total_confirmed_cases.log,col=type))+
geom_point(size=3)+
default_theme+
theme(legend.position = "bottom")+
#geom_abline(slope = slope,intercept =intercept,lty=2)+
#facet_wrap(~case_type,ncol=1)+
scale_color_manual(values = c("Historical"="#377eb8","Predicted"="#e41a1c")))
write_plot(historical_model_predictions.plot,wd=results_dir)
Q3: What is the effect on social distancing, descreased mobility on case load?
Load data from Google which compoutes % change in user mobility relative to baseline for * Recreation
* Workplace
* Residence
* Park
* Grocery
Data from https://www.google.com/covid19/mobility/
# See pre-processing section for script on gathering mobility data
# UNDER DEVELOPMENT
mobility<-read.csv("/Users/stevensmith/Projects/MIT_COVID19/mobility.csv",header = T,stringsAsFactors = F)
#mobility$Retail_Recreation<-as.numeric(sub(mobility$Retail_Recreation,pattern = "%",replacement = ""))
#mobility$Workplace<-as.numeric(sub(mobility$Workplace,pattern = "%",replacement = ""))
#mobility$Residential<-as.numeric(sub(mobility$Residential,pattern = "%",replacement = ""))
##------------------------------------------
## Show relationship between mobility and caseload
##------------------------------------------
mobility$County<-gsub(mobility$County,pattern = " County",replacement = "")
Corona_Cases.US_state.mobility<-merge(Corona_Cases.US_state,plyr::rename(mobility,c("State"="Province.State","County"="City")))
#Corona_Cases.US_state.tmp<-merge(metadata,Corona_Cases.US_state.tmp)
# Needs to happen upsteam, see todos
#Corona_Cases.US_state.tmp$Total_confirmed_cases.perperson<-Corona_Cases.US_state.tmp$Total_confirmed_cases/as.numeric(Corona_Cases.US_state.tmp$Population)
mobility_measures<-c("Retail_Recreation","Grocery_Pharmacy","Parks","Transit","Workplace","Residential")
plot_data<-filter(Corona_Cases.US_state.mobility, Date.numeric==max(Corona_Cases.US_state$Date.numeric) ) %>% melt(measure.vars=mobility_measures)
plot_data$value<-as.numeric(gsub(plot_data$value,pattern = "%",replacement = ""))
plot_data<-filter(plot_data,!is.na(value))
(mobility.plot<-ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
facet_grid(Province.State~variable,scales = "free")+
xlab("Mobility change from baseline (%)")+
ylab(paste0("Confirmed cases per 100 people(Today)"))+
default_theme+
ggtitle("Mobility change vs cases"))

(mobility.global.plot<-ggplot(plot_data,aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
facet_wrap(~variable,scales = "free")+
xlab("Mobility change from baseline (%)")+
ylab(paste0("Confirmed cases (Today) per 100 people"))+
default_theme+
ggtitle("Mobility change vs cases"))

plot_data.permobility_summary<-ddply(plot_data,c("Province.State","variable"),summarise,cor=cor(y =Total_confirmed_cases.per100,x=value),median_change=median(x=value)) %>% arrange(-abs(cor))
kable(plot_data.permobility_summary,caption = "Ranked per-state mobility correlation with total confirmed cases")
Ranked per-state mobility correlation with total confirmed cases
| Alaska |
Transit |
1.0000000 |
-63.0 |
| Delaware |
Retail_Recreation |
1.0000000 |
-39.5 |
| Delaware |
Grocery_Pharmacy |
1.0000000 |
-17.5 |
| Delaware |
Parks |
-1.0000000 |
20.5 |
| Delaware |
Transit |
1.0000000 |
-37.0 |
| Delaware |
Workplace |
1.0000000 |
-37.0 |
| Delaware |
Residential |
-1.0000000 |
14.0 |
| Hawaii |
Grocery_Pharmacy |
0.9947636 |
-34.0 |
| Hawaii |
Retail_Recreation |
0.9729533 |
-56.0 |
| New Hampshire |
Parks |
0.9510318 |
-20.0 |
| Vermont |
Parks |
0.9354611 |
-35.5 |
| Maine |
Transit |
-0.9149105 |
-50.0 |
| Hawaii |
Parks |
0.8909425 |
-72.0 |
| Connecticut |
Grocery_Pharmacy |
-0.8892043 |
-6.0 |
| Utah |
Residential |
-0.8763111 |
12.0 |
| Hawaii |
Transit |
0.8505994 |
-89.0 |
| Utah |
Transit |
-0.8459559 |
-18.0 |
| South Dakota |
Parks |
0.8024174 |
-26.0 |
| Arizona |
Grocery_Pharmacy |
-0.7764535 |
-15.0 |
| Wyoming |
Parks |
-0.7722270 |
-4.0 |
| Rhode Island |
Workplace |
-0.7677330 |
-39.5 |
| Connecticut |
Transit |
-0.7652498 |
-50.0 |
| Massachusetts |
Workplace |
-0.7520500 |
-39.0 |
| Alaska |
Grocery_Pharmacy |
-0.7441638 |
-7.0 |
| Alaska |
Workplace |
-0.7158284 |
-33.0 |
| Arizona |
Residential |
0.7144825 |
13.0 |
| Alaska |
Residential |
0.6709091 |
13.0 |
| Nevada |
Transit |
-0.6555946 |
-20.0 |
| New York |
Workplace |
-0.6529864 |
-34.5 |
| Vermont |
Grocery_Pharmacy |
-0.6527134 |
-25.0 |
| North Dakota |
Parks |
0.6435521 |
-34.0 |
| Rhode Island |
Retail_Recreation |
-0.6311475 |
-45.0 |
| Utah |
Parks |
-0.6164639 |
17.0 |
| New Jersey |
Parks |
-0.6032447 |
-6.0 |
| Rhode Island |
Residential |
-0.5983355 |
18.5 |
| Idaho |
Residential |
-0.5981256 |
11.0 |
| Maine |
Workplace |
-0.5918860 |
-30.0 |
| New York |
Retail_Recreation |
-0.5899778 |
-46.0 |
| Nebraska |
Workplace |
0.5818339 |
-32.0 |
| Utah |
Workplace |
-0.5549784 |
-37.0 |
| Arizona |
Retail_Recreation |
-0.5397469 |
-42.5 |
| New Jersey |
Workplace |
-0.5329491 |
-44.0 |
| New York |
Parks |
0.5293822 |
20.0 |
| Connecticut |
Retail_Recreation |
-0.5285466 |
-45.0 |
| Hawaii |
Residential |
-0.5258143 |
19.0 |
| Connecticut |
Residential |
0.5222568 |
14.0 |
| Massachusetts |
Retail_Recreation |
-0.4963927 |
-44.0 |
| Arkansas |
Parks |
0.4919159 |
-12.0 |
| Maine |
Parks |
0.4904383 |
-31.0 |
| Connecticut |
Workplace |
-0.4866511 |
-39.0 |
| New Jersey |
Grocery_Pharmacy |
-0.4865297 |
2.5 |
| New Mexico |
Grocery_Pharmacy |
-0.4833580 |
-11.0 |
| West Virginia |
Parks |
0.4687649 |
-33.0 |
| Nebraska |
Residential |
-0.4658619 |
14.0 |
| Arizona |
Transit |
0.4656115 |
-38.0 |
| Iowa |
Parks |
-0.4627565 |
28.5 |
| Montana |
Parks |
-0.4624845 |
-58.0 |
| New Mexico |
Residential |
0.4560046 |
13.5 |
| Maryland |
Workplace |
-0.4547760 |
-35.0 |
| Rhode Island |
Parks |
0.4481230 |
52.0 |
| Kansas |
Parks |
0.4404620 |
72.0 |
| North Dakota |
Retail_Recreation |
-0.4392048 |
-42.0 |
| Illinois |
Transit |
-0.4282323 |
-31.0 |
| New Jersey |
Retail_Recreation |
-0.4232441 |
-62.5 |
| Pennsylvania |
Workplace |
-0.4197047 |
-36.0 |
| Montana |
Residential |
0.4142652 |
14.0 |
| New Mexico |
Parks |
0.4108262 |
-31.5 |
| Massachusetts |
Grocery_Pharmacy |
-0.4080797 |
-7.0 |
| South Carolina |
Workplace |
0.4074137 |
-30.0 |
| Maryland |
Grocery_Pharmacy |
-0.4072276 |
-10.0 |
| New Jersey |
Transit |
-0.4068095 |
-50.5 |
| Vermont |
Residential |
0.4056732 |
11.5 |
| New Hampshire |
Residential |
-0.3957885 |
14.0 |
| Kentucky |
Parks |
-0.3956341 |
28.5 |
| Pennsylvania |
Retail_Recreation |
-0.3931098 |
-45.0 |
| Michigan |
Parks |
0.3872308 |
28.5 |
| Alabama |
Workplace |
-0.3841913 |
-29.0 |
| New Mexico |
Retail_Recreation |
-0.3729156 |
-42.5 |
| Oregon |
Parks |
-0.3683446 |
16.5 |
| New York |
Transit |
-0.3672879 |
-48.0 |
| Alabama |
Grocery_Pharmacy |
-0.3672738 |
-2.0 |
| Missouri |
Residential |
-0.3540422 |
13.0 |
| Nebraska |
Grocery_Pharmacy |
0.3419487 |
-0.5 |
| Wisconsin |
Transit |
-0.3384426 |
-23.5 |
| North Dakota |
Workplace |
0.3322395 |
-40.0 |
| Arkansas |
Retail_Recreation |
-0.3269512 |
-30.0 |
| Maryland |
Retail_Recreation |
-0.3211834 |
-39.0 |
| Idaho |
Workplace |
-0.3191705 |
-29.0 |
| Virginia |
Transit |
-0.3175134 |
-33.0 |
| Idaho |
Grocery_Pharmacy |
-0.3168106 |
-5.5 |
| California |
Residential |
0.3084436 |
14.0 |
| Montana |
Transit |
0.3074741 |
-41.0 |
| California |
Transit |
-0.3045167 |
-42.0 |
| Maine |
Retail_Recreation |
-0.3038669 |
-42.0 |
| Nevada |
Residential |
0.3027008 |
17.0 |
| California |
Parks |
-0.2991494 |
-38.5 |
| Illinois |
Workplace |
-0.2933922 |
-30.5 |
| Colorado |
Residential |
0.2929478 |
14.0 |
| Wyoming |
Grocery_Pharmacy |
-0.2926152 |
-10.0 |
| South Carolina |
Parks |
-0.2911161 |
-23.0 |
| Florida |
Residential |
0.2901461 |
14.0 |
| Alaska |
Retail_Recreation |
0.2875349 |
-39.0 |
| Minnesota |
Transit |
-0.2865186 |
-28.5 |
| Idaho |
Transit |
-0.2845171 |
-30.0 |
| Wyoming |
Workplace |
-0.2830794 |
-31.0 |
| Pennsylvania |
Parks |
0.2792139 |
12.0 |
| Vermont |
Retail_Recreation |
0.2782373 |
-57.0 |
| Oregon |
Grocery_Pharmacy |
-0.2775639 |
-7.0 |
| Wyoming |
Transit |
-0.2689419 |
-17.0 |
| North Carolina |
Grocery_Pharmacy |
0.2685473 |
0.0 |
| North Carolina |
Workplace |
0.2655352 |
-31.0 |
| Pennsylvania |
Grocery_Pharmacy |
-0.2654345 |
-6.0 |
| Georgia |
Grocery_Pharmacy |
-0.2633300 |
-10.0 |
| Kansas |
Workplace |
0.2609616 |
-32.5 |
| Vermont |
Workplace |
-0.2601713 |
-43.0 |
| Rhode Island |
Grocery_Pharmacy |
0.2579082 |
-7.5 |
| Maryland |
Residential |
0.2559636 |
15.0 |
| Alabama |
Transit |
-0.2548113 |
-36.5 |
| Mississippi |
Residential |
0.2511330 |
13.0 |
| Illinois |
Parks |
0.2500433 |
26.5 |
| Rhode Island |
Transit |
-0.2479675 |
-56.0 |
| Tennessee |
Workplace |
-0.2478116 |
-31.0 |
| Texas |
Workplace |
0.2477673 |
-32.0 |
| West Virginia |
Grocery_Pharmacy |
-0.2414539 |
-6.0 |
| Florida |
Parks |
-0.2400281 |
-43.0 |
| Tennessee |
Residential |
0.2387085 |
11.5 |
| Washington |
Grocery_Pharmacy |
0.2362768 |
-7.0 |
| Wisconsin |
Parks |
0.2322063 |
51.5 |
| Georgia |
Workplace |
-0.2302457 |
-33.5 |
| New York |
Grocery_Pharmacy |
-0.2289018 |
8.0 |
| Texas |
Residential |
-0.2248526 |
15.0 |
| Minnesota |
Parks |
0.2236673 |
-9.0 |
| Missouri |
Workplace |
0.2225327 |
-29.0 |
| Georgia |
Retail_Recreation |
-0.2185902 |
-41.0 |
| Hawaii |
Workplace |
0.2184238 |
-46.0 |
| Connecticut |
Parks |
0.2152508 |
43.0 |
| South Dakota |
Workplace |
0.2147216 |
-35.0 |
| Nevada |
Retail_Recreation |
-0.2103494 |
-43.0 |
| Arizona |
Parks |
-0.2090472 |
-44.5 |
| North Carolina |
Transit |
0.2083002 |
-32.0 |
| West Virginia |
Workplace |
0.2056735 |
-33.0 |
| Oregon |
Residential |
-0.2048459 |
10.5 |
| North Dakota |
Grocery_Pharmacy |
-0.2020507 |
-8.0 |
| Indiana |
Parks |
-0.2008507 |
29.0 |
| Nevada |
Workplace |
-0.1999439 |
-40.0 |
| Kansas |
Grocery_Pharmacy |
-0.1999298 |
-14.0 |
| Nebraska |
Transit |
-0.1949971 |
-9.0 |
| Mississippi |
Grocery_Pharmacy |
-0.1934143 |
-8.0 |
| Illinois |
Residential |
0.1916707 |
14.0 |
| Colorado |
Parks |
-0.1916413 |
2.0 |
| Tennessee |
Parks |
-0.1907621 |
10.5 |
| Kentucky |
Transit |
-0.1823871 |
-31.0 |
| Virginia |
Residential |
0.1815482 |
14.0 |
| Alabama |
Parks |
0.1806021 |
-1.0 |
| North Carolina |
Residential |
0.1801741 |
13.0 |
| Wisconsin |
Residential |
-0.1784243 |
14.0 |
| Texas |
Parks |
0.1734117 |
-42.0 |
| Kentucky |
Grocery_Pharmacy |
0.1724719 |
4.0 |
| Utah |
Retail_Recreation |
-0.1721727 |
-40.0 |
| Pennsylvania |
Transit |
-0.1707970 |
-42.0 |
| Idaho |
Retail_Recreation |
-0.1689539 |
-39.5 |
| New Hampshire |
Retail_Recreation |
-0.1680346 |
-41.0 |
| Indiana |
Residential |
0.1647059 |
12.0 |
| Ohio |
Transit |
0.1614432 |
-28.0 |
| Massachusetts |
Parks |
0.1612323 |
39.0 |
| South Dakota |
Residential |
0.1586762 |
15.0 |
| New Jersey |
Residential |
0.1561462 |
18.0 |
| New Mexico |
Transit |
0.1501835 |
-38.5 |
| Iowa |
Transit |
0.1472111 |
-24.0 |
| Minnesota |
Retail_Recreation |
0.1386392 |
-40.5 |
| Michigan |
Workplace |
-0.1385343 |
-40.0 |
| North Dakota |
Transit |
0.1383314 |
-48.0 |
| North Dakota |
Residential |
-0.1377901 |
17.0 |
| Texas |
Grocery_Pharmacy |
0.1367169 |
-14.0 |
| Virginia |
Grocery_Pharmacy |
-0.1356063 |
-8.0 |
| Missouri |
Parks |
0.1341780 |
0.0 |
| Indiana |
Retail_Recreation |
0.1308234 |
-38.0 |
| California |
Grocery_Pharmacy |
-0.1304283 |
-11.5 |
| Missouri |
Transit |
-0.1252887 |
-24.5 |
| Mississippi |
Retail_Recreation |
-0.1250649 |
-40.0 |
| Arkansas |
Residential |
0.1228100 |
12.0 |
| Wisconsin |
Grocery_Pharmacy |
0.1200599 |
-1.0 |
| North Carolina |
Retail_Recreation |
0.1195394 |
-34.0 |
| Oklahoma |
Parks |
0.1192417 |
-18.5 |
| West Virginia |
Residential |
-0.1190854 |
11.0 |
| Idaho |
Parks |
0.1188234 |
-22.0 |
| Kentucky |
Residential |
0.1166247 |
12.0 |
| Wisconsin |
Workplace |
-0.1159645 |
-31.0 |
| Florida |
Retail_Recreation |
0.1134600 |
-43.0 |
| California |
Retail_Recreation |
-0.1115360 |
-44.0 |
| Utah |
Grocery_Pharmacy |
0.1101583 |
-4.0 |
| Montana |
Retail_Recreation |
0.1082546 |
-50.0 |
| Montana |
Workplace |
-0.1077526 |
-40.0 |
| Wyoming |
Residential |
0.1076194 |
12.5 |
| Michigan |
Residential |
0.1070626 |
15.0 |
| Maryland |
Transit |
-0.1066350 |
-39.0 |
| Nebraska |
Retail_Recreation |
0.1064528 |
-36.0 |
| Mississippi |
Parks |
-0.1055566 |
-25.0 |
| Arkansas |
Workplace |
-0.1049399 |
-26.0 |
| Oklahoma |
Grocery_Pharmacy |
-0.1043888 |
-0.5 |
| Kansas |
Transit |
-0.1035855 |
-26.5 |
| Massachusetts |
Transit |
-0.1033202 |
-45.0 |
| Oregon |
Retail_Recreation |
0.1027857 |
-40.5 |
| Iowa |
Workplace |
0.1018614 |
-30.0 |
| New Hampshire |
Grocery_Pharmacy |
-0.1008691 |
-6.0 |
| Massachusetts |
Residential |
0.1003879 |
15.0 |
| Virginia |
Parks |
0.0999071 |
6.0 |
| Alabama |
Retail_Recreation |
0.0991263 |
-39.0 |
| Mississippi |
Transit |
-0.0963377 |
-38.5 |
| Michigan |
Transit |
0.0956990 |
-46.0 |
| Indiana |
Workplace |
0.0941518 |
-34.0 |
| Minnesota |
Grocery_Pharmacy |
0.0937549 |
-6.0 |
| Oregon |
Workplace |
-0.0933846 |
-31.0 |
| Michigan |
Retail_Recreation |
-0.0918149 |
-53.0 |
| New York |
Residential |
0.0907508 |
17.5 |
| Ohio |
Residential |
0.0901676 |
14.0 |
| Iowa |
Grocery_Pharmacy |
-0.0883928 |
4.0 |
| Oklahoma |
Workplace |
0.0878819 |
-31.0 |
| West Virginia |
Transit |
-0.0873222 |
-45.0 |
| Alabama |
Residential |
-0.0858002 |
11.0 |
| Texas |
Transit |
0.0829276 |
-41.0 |
| Georgia |
Residential |
-0.0823809 |
13.0 |
| Virginia |
Retail_Recreation |
-0.0796062 |
-35.0 |
| Nevada |
Parks |
0.0786215 |
-12.5 |
| Pennsylvania |
Residential |
0.0777935 |
15.0 |
| South Dakota |
Transit |
-0.0770130 |
-40.0 |
| Virginia |
Workplace |
-0.0767439 |
-32.0 |
| Minnesota |
Residential |
-0.0757907 |
17.0 |
| Oregon |
Transit |
0.0756402 |
-27.5 |
| Kentucky |
Retail_Recreation |
0.0751733 |
-29.0 |
| Florida |
Transit |
-0.0748017 |
-49.0 |
| Ohio |
Parks |
-0.0742580 |
67.5 |
| Georgia |
Parks |
0.0739677 |
-6.0 |
| Ohio |
Grocery_Pharmacy |
0.0718866 |
0.0 |
| Texas |
Retail_Recreation |
0.0623424 |
-40.0 |
| Maine |
Residential |
-0.0616120 |
11.0 |
| Vermont |
Transit |
-0.0583184 |
-63.0 |
| Minnesota |
Workplace |
-0.0568999 |
-33.0 |
| Washington |
Transit |
-0.0564257 |
-33.5 |
| North Carolina |
Parks |
-0.0559940 |
7.0 |
| Florida |
Grocery_Pharmacy |
0.0547354 |
-14.0 |
| Colorado |
Transit |
0.0496487 |
-36.0 |
| Ohio |
Retail_Recreation |
0.0487401 |
-36.0 |
| Georgia |
Transit |
-0.0472682 |
-35.0 |
| South Dakota |
Retail_Recreation |
-0.0471844 |
-39.0 |
| New Hampshire |
Workplace |
0.0469066 |
-37.0 |
| California |
Workplace |
-0.0457712 |
-36.0 |
| Washington |
Retail_Recreation |
0.0456369 |
-42.0 |
| Kentucky |
Workplace |
-0.0438541 |
-36.5 |
| Indiana |
Transit |
0.0426119 |
-29.0 |
| Iowa |
Retail_Recreation |
0.0394486 |
-38.0 |
| Ohio |
Workplace |
-0.0374366 |
-35.0 |
| Illinois |
Grocery_Pharmacy |
-0.0353067 |
2.0 |
| Arkansas |
Grocery_Pharmacy |
-0.0349518 |
3.0 |
| Washington |
Parks |
0.0341708 |
-3.5 |
| Missouri |
Retail_Recreation |
-0.0340360 |
-36.0 |
| Tennessee |
Transit |
0.0333686 |
-32.0 |
| Arizona |
Workplace |
-0.0324137 |
-35.0 |
| Nebraska |
Parks |
0.0313258 |
55.5 |
| New Hampshire |
Transit |
0.0312727 |
-57.0 |
| Colorado |
Grocery_Pharmacy |
-0.0307375 |
-17.0 |
| New Mexico |
Workplace |
0.0303356 |
-34.0 |
| Washington |
Workplace |
-0.0297964 |
-38.0 |
| Colorado |
Retail_Recreation |
-0.0281544 |
-44.0 |
| Wisconsin |
Retail_Recreation |
0.0276291 |
-44.0 |
| West Virginia |
Retail_Recreation |
-0.0262738 |
-38.5 |
| South Carolina |
Grocery_Pharmacy |
0.0261216 |
1.0 |
| Washington |
Residential |
0.0252693 |
13.0 |
| Maine |
Grocery_Pharmacy |
-0.0240360 |
-13.0 |
| Michigan |
Grocery_Pharmacy |
-0.0226211 |
-11.0 |
| Tennessee |
Retail_Recreation |
-0.0201555 |
-30.0 |
| Illinois |
Retail_Recreation |
0.0194795 |
-40.0 |
| Indiana |
Grocery_Pharmacy |
-0.0181920 |
-5.5 |
| South Carolina |
Residential |
-0.0179439 |
12.0 |
| South Carolina |
Transit |
-0.0173707 |
-45.0 |
| Maryland |
Parks |
0.0171922 |
27.0 |
| Tennessee |
Grocery_Pharmacy |
0.0159403 |
6.0 |
| Wyoming |
Retail_Recreation |
0.0158178 |
-39.0 |
| Mississippi |
Workplace |
-0.0152403 |
-33.0 |
| Kansas |
Residential |
-0.0150239 |
13.0 |
| South Dakota |
Grocery_Pharmacy |
-0.0138903 |
-9.0 |
| Colorado |
Workplace |
0.0125797 |
-39.0 |
| Oklahoma |
Transit |
0.0115592 |
-26.0 |
| Iowa |
Residential |
-0.0101969 |
13.0 |
| Oklahoma |
Residential |
0.0098935 |
15.0 |
| South Carolina |
Retail_Recreation |
0.0078966 |
-35.0 |
| Montana |
Grocery_Pharmacy |
-0.0073289 |
-16.0 |
| Florida |
Workplace |
0.0063612 |
-33.0 |
| Kansas |
Retail_Recreation |
-0.0060651 |
-38.0 |
| Missouri |
Grocery_Pharmacy |
0.0049274 |
2.0 |
| Arkansas |
Transit |
-0.0034922 |
-27.0 |
| Oklahoma |
Retail_Recreation |
-0.0030671 |
-31.0 |
| Nevada |
Grocery_Pharmacy |
-0.0015128 |
-12.5 |
| Alaska |
Parks |
NA |
29.0 |
| District of Columbia |
Retail_Recreation |
NA |
-69.0 |
| District of Columbia |
Grocery_Pharmacy |
NA |
-28.0 |
| District of Columbia |
Parks |
NA |
-65.0 |
| District of Columbia |
Transit |
NA |
-69.0 |
| District of Columbia |
Workplace |
NA |
-48.0 |
| District of Columbia |
Residential |
NA |
17.0 |
# sanity check
ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(x=Total_confirmed_cases.per100,fill=variable))+geom_histogram()+
facet_grid(~Province.State)+
default_theme+
theme(legend.position = "bottom")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

write_plot(mobility.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.plot.png"
write_plot(mobility.global.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.global.plot.png"
(plot_data.permobility_summary.plot<-ggplot(plot_data.permobility_summary,aes(x=variable,y=median_change))+
geom_jitter(size=2,width=.2)+
#geom_jitter(data=plot_data.permobility_summary %>% arrange(-abs(median_change)) %>% head(n=15),aes(col=Province.State),size=2,width=.2)+
default_theme+
ggtitle("Per-Sate Median Change in Mobility")+
xlab("Mobility Meaure")+
ylab("Median Change from Baseline"))

write_plot(plot_data.permobility_summary.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/plot_data.permobility_summary.plot.png"